Healthcare organizations don’t struggle to pilot AI—they struggle to trust it in production. This talk lays out a practical blueprint for building high-trust healthcare AI systems that can scale across teams while meeting privacy and compliance requirements. We’ll cover the three pillars that determine deployment success: (1) privacy by design—regulatory-grade de-identification across unstructured text, PDFs, and medical imaging; (2) performance you can prove—benchmarking, evaluation for information extraction and summarization, and continuous quality monitoring under real-world drift; and (3) platform thinking—human-in-the-loop workflows, no-code tooling, model governance, and reusable components that accelerate delivery without sacrificing safety. Attendees will leave with an actionable reference architecture, key design controls, and a checklist for moving from isolated demos to reliable, auditable healthcare AI at enterprise scale.